基于SPARQL的映射管理

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-01 DOI:10.1109/ICOSC.2015.7050851

Alan Meehan, Rob Brennan, D. O’Sullivan

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引用次数: 3

摘要

关联数据(LD)云由涵盖各种主题的LD源组成。这些数据源使用正式词汇表来表示它们的数据，在许多情况下，它们使用异构词汇表来表示关于相同主题的数据。必须克服这种数据异构性，才能有效地集成和使用来自LD Cloud的数据。映射通过将异构源数据转换为通用目标词汇表来克服这种数据异构性。随着新数据源的出现和现有数据源的变化，必须创建新的映射并维护现有的映射。这些映射的管理是一个重要的问题，但经常被忽视。缺乏映射管理方法降低了为共享、重用和维护目的查找映射的便利性。本文提出了一种基于SPARQL的映射管理方法(SBMM)。SBMM方法涉及使用SPARQL查询对基于rdf的映射表示执行分析和维护。我们展示了一项实验的结果，该实验比较了我们之前设计的基于rdf的映射表示(称为SPARQL Centric mapping (SCM)表示)与R2R映射语言的分析能力。

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SPARQL based mapping management

The Linked Data (LD) Cloud consists of LD sources covering a wide variety of topics. These data sources use formal vocabularies to represent their data and in many cases, they use heterogeneous vocabularies to represent data about the same topics. This data heterogeneity must be overcome to effectively integrate and consume data from the LD Cloud. Mappings overcome this data heterogeneity by transforming heterogeneous source data to a common target vocabulary. As new data sources emerge and existing ones change over time, new mappings must be created and existing ones maintained. Management of these mappings is an important issue but often neglected. Lack of a mapping management method decreases the ease of finding mappings for sharing, reuse and maintenance purposes. In this paper we present a method for the management of mappings between LD sources - SPARQL Based Mapping Management (SBMM). The SBMM method involves the use of SPARQL queries to perform analysis and maintenance over an RDF-based mapping representation. We present the results from an experiment that compared the analytical affordance of an RDF-based mapping representation we previously devised, called the SPARQL Centric Mapping (SCM) representation, compared to the R2R Mapping Language.

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Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

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